Recursive self-improvement as an empirical research program.
The claim under examination is narrow: systems that improve the process by which they themselves improve — not merely agents that retry within a task, and not merely human-supervised iteration at higher throughput. That distinction matters, because most of what is currently labeled RSI sits somewhere between those two.
This session treats the problem as a stack with separable research questions: the models and methods that generate improvement, the feedback and evaluation that verify the improvement is real, and the infrastructure that lets the loop run without a human in the middle.
Talks stay short. Discussion is the point. Papers and materials are circulated in advance. Speakers are researchers operating inside these loops; the room is expected to press on assumptions, measurement, and what would count as decisive evidence.
Co-hosted with Inventors Residency of San Francisco.
The Frontier Research Club is a curated forum for rigorous, technical discussion at the frontier of AI. We convene researchers from the frontier labs, Stanford, Berkeley, and the teams building in production to examine concrete work — papers, methods, and results — with a bias toward assumptions, evaluation methodology, failure modes and convincing evidence.
Presentations are intentionally brief so the majority of time is reserved for questions and critique. Materials are shared in advance so the conversation starts at depth.
Agenda
5:30pm: Doors open
5:30pm – 6:30pm: Networking + light dinner
6:30pm – 8:00pm: Research presentations + discussion
8:00pm – 8:30pm: Networking
Presenters & topics
Talk 1: FutureSim — Replaying World Events to Evaluate Adaptive Agents
Shashwat Goel — PhD Researcher, ELLIS / Max Planck Institute Tübingen · Best Paper, ICML Forecasting Workshop · AAAI Outstanding Paper Award
Shashwat works on the science of evaluations and how AI can iteratively improve — the exact hinge of this session. His PhD at Max